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Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming

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  • Colvin, Matthew
  • Maravelias, Christos T.

Abstract

We discuss methods for the solution of a multi-stage stochastic programming formulation for the resource-constrained scheduling of clinical trials in the pharmaceutical research and development pipeline. First, we present a number of theoretical properties to reduce the size and improve the tightness of the formulation, focusing primarily on non-anticipativity constraints. Second, we develop a novel branch and cut algorithm where necessary non-anticipativity constraints that are unlikely to be active are removed from the initial formulation and only added if they are violated within the search tree. We improve the performance of our algorithm by combining different node selection strategies and exploring different approaches to constraint violation checking.

Suggested Citation

  • Colvin, Matthew & Maravelias, Christos T., 2010. "Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming," European Journal of Operational Research, Elsevier, vol. 203(1), pages 205-215, May.
  • Handle: RePEc:eee:ejores:v:203:y:2010:i:1:p:205-215
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    References listed on IDEAS

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    Cited by:

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    2. Giovanni Pantuso, 2021. "A node formulation for multistage stochastic programs with endogenous uncertainty," Computational Management Science, Springer, vol. 18(3), pages 325-354, July.
    3. Maier, Sebastian & Pflug, Georg C. & Polak, John W., 2020. "Valuing portfolios of interdependent real options under exogenous and endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 285(1), pages 133-147.
    4. Lars Hellemo & Paul I. Barton & Asgeir Tomasgard, 2018. "Decision-dependent probabilities in stochastic programs with recourse," Computational Management Science, Springer, vol. 15(3), pages 369-395, October.
    5. Suhan Wu & Min Luo & Jingxia Zhang & Daoheng Zhang & Lianmin Zhang, 2022. "Pharmaceutical Supply Chain in China: Pricing and Production Decisions with Price-Sensitive and Uncertain Demand," Sustainability, MDPI, vol. 14(13), pages 1-28, June.
    6. Peter Buchholz & Dimitri Scheftelowitsch, 2019. "Computation of weighted sums of rewards for concurrent MDPs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(1), pages 1-42, February.
    7. Tahir Ekin & Nicholas G. Polson & Refik Soyer, 2017. "Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(8), pages 613-627, December.
    8. Kavitha G. Menon & Ricardo Fukasawa & Luis A. Ricardez-Sandoval, 2021. "A novel stochastic programming approach for scheduling of batch processes with decision dependent time of uncertainty realization," Annals of Operations Research, Springer, vol. 305(1), pages 163-190, October.
    9. Abdoli, B. & Hooshmand, F. & MirHassani, S.A., 2023. "A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem," Applied Energy, Elsevier, vol. 338(C).
    10. Feng, Wei & Feng, Yiping & Zhang, Qi, 2021. "Multistage robust mixed-integer optimization under endogenous uncertainty," European Journal of Operational Research, Elsevier, vol. 294(2), pages 460-475.
    11. F. Hooshmand & S. A. MirHassani, 2018. "Reduction of nonanticipativity constraints in multistage stochastic programming problems with endogenous and exogenous uncertainty," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(1), pages 1-18, February.
    12. Salo, Ahti & Andelmin, Juho & Oliveira, Fabricio, 2022. "Decision programming for mixed-integer multi-stage optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(2), pages 550-565.
    13. Fleischhacker, Adam J. & Zhao, Yao, 2011. "Planning for demand failure: A dynamic lot size model for clinical trial supply chains," European Journal of Operational Research, Elsevier, vol. 211(3), pages 496-506, June.
    14. Colvin, Matthew & Maravelias, Christos T., 2011. "R&D pipeline management: Task interdependencies and risk management," European Journal of Operational Research, Elsevier, vol. 215(3), pages 616-628, December.
    15. Solak, Senay & Clarke, John-Paul B. & Johnson, Ellis L. & Barnes, Earl R., 2010. "Optimization of R&D project portfolios under endogenous uncertainty," European Journal of Operational Research, Elsevier, vol. 207(1), pages 420-433, November.

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